Showing papers on "Speckle noise published in 2016"
TL;DR: The capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition is investigated, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose.
Abstract: Many methods have been proposed to improve the performance of synthetic aperture radar (SAR) target recognition but seldom consider the issues in real-world recognition systems, such as the invariance under target translation, the invariance under speckle variation in different observations, and the tolerance of pose missing in training data. In this letter, we investigate the capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition. Experimental results demonstrate the effectiveness and efficiency of the proposed method. The best performance is obtained by using the CNN trained by all types of augmentation operations, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose.
582 citations
TL;DR: This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet that exploits representative neighborhood features from each pixel using PCA filters as convolutional filters to generate change maps with less noise spots.
Abstract: This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy $c$ -means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.
207 citations
TL;DR: The results indicate that this method can effectively reduce the speckle in the reconstruction in 3-D holographic display and is free of iteration which allows improving the image quality and the calculation speed at the same time.
Abstract: The purpose of this study is to implement speckle reduced three-dimensional (3-D) holographic display by single phase-only spatial light modulator (SLM). The complex amplitude of hologram is transformed to pure phase value based on double-phase method. To suppress noises and higher order diffractions, we introduced a 4-f system with a filter at the frequency plane. A blazing grating is proposed to separate the complex amplitude on the frequency plane. Due to the complex modulation, the speckle noise is reduced. Both computer simulation and optical experiment have been conducted to verify the effectiveness of the method. The results indicate that this method can effectively reduce the speckle in the reconstruction in 3-D holographic display. Furthermore, the method is free of iteration which allows improving the image quality and the calculation speed at the same time.
114 citations
TL;DR: The experimental results obtained show that the proposed SAR image change detection method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.
Abstract: Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.
110 citations
TL;DR: The probability distributions is regarded as a new probabilistic metric and introduced to k-nearest neighbor to improve the accuracy of classification based on superpixels, which takes spatial relationship between pixels into consideration, and it is robust to speckle noise.
Abstract: A new polarimetric synthetic aperture radar (PolSAR) images classification method based on multilayer autoencoders and superpixels is proposed in this paper. First, in order to explore the spatial relations between pixels in PolSAR data, the RGB image formed with Pauli decomposition is used to produce superpixels to integrate contextual information of neighborhood. Second, multilayer autoencoders network is used to learning the features used for distinguishing the multiple categories for each pixel, and a softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. Finally, the probability distributions is regarded as a new probabilistic metric and introduced to k-nearest neighbor to improve the accuracy of classification based on superpixels, which takes spatial relationship between pixels into consideration, and it is robust to speckle noise. The proposed method makes good use of the scattering characteristics in each pixel and spatial information of PolSAR data. Compared with other state-of-the-art methods, the results of proposed method show better agreement with the ground truth and significant improvement in classification accuracy and discriminability of small differences between different categories.
110 citations
TL;DR: A review of the laser speckle theory used in biomedical applications is presented and the practical concepts that are useful in the construction of laser Speckle imagers are reviewed.
Abstract: Laser speckle is a complex interference phenomenon that can easily be understood, in concept, but is difficult to predict mathematically, because it is a stochastic process. The use of laser speckle to produce images, which can carry many types of information, is called laser speckle imaging (LSI). The biomedical applications of LSI started in 1981 and, since then, many scientists have improved the laser speckle theory and developed different imaging techniques. During this process, some inconsistencies have been propagated up to now. These inconsistencies should be clarified in order to avoid errors in future works. This review presents a review of the laser speckle theory used in biomedical applications. Moreover, we also make a review of the practical concepts that are useful in the construction of laser speckle imagers. This study is not only an exposition of the concepts that can be found in the literature but also a critical analysis of the investigations presented so far. Concepts like scatterers velocity distribution, effect of static scatterers, optimal speckle size, light penetration angle, and contrast computation algorithms are discussed in detail.
94 citations
TL;DR: The basic principles of optoacoustic signal generation and image formation for objects ranging from individual sub-resolution absorbers to a continuous absorption distribution are described and may also serve as a basis for optimal design of tomographic acquisition geometries andimage formation strategies.
92 citations
TL;DR: A novel second order total generalised variation (TGV) decomposition model is proposed to remove noise from the OCT image by considering the speckle noise in the image as texture or oscillatory patterns and can eliminate the staircase side effect in the resulting denoised image.
91 citations
TL;DR: Sparse Representation-based Classification is utilized to classify a 10-class moving and stationary target acquisition and recognition (MSTAR) target, which is a standard SAR data set, and experimental results demonstrate the good performance of SRC.
Abstract: Recent years have witnessed an ever-mounting interest in the research of sparse representation. The framework, Sparse Representation-based Classification (SRC), has been widely applied as a classifier in numerous domains, among which Synthetic Aperture Radar (SAR) target recognition is really challenging because it still is an open problem to interpreting the SAR image. In this paper, SRC is utilized to classify a 10-class moving and stationary target acquisition and recognition (MSTAR) target, which is a standard SAR data set. Before the classification, the sizes of the images need to be normalized to maintain the useful information, target and shadow, and to suppress the speckle noise. Specifically, a preprocessing method is recommended to extract the feature vectors of the image, and the feature vectors of the test samples can be represented by the sparse linear combination of basis vectors generated by the feature vectors of the training samples. Then the sparse representation is solved by l 1 -norm minimization. Finally, the identities of the test samples are inferred by the reconstructive errors calculated through the sparse coefficient. Experimental results demonstrate the good performance of SRC. Additionally, the average recognition rate under different feature spaces and the recognition rate of each target are discussed.
89 citations
TL;DR: A new approach based on non-local means (NLM) method is proposed to remove the speckle noise in the US images, which outperforms other related well-accepted methods, both in terms of objective and subjective evaluations.
85 citations
TL;DR: This study combines local statistics with the NLM filter to reduce speckle in ultrasound images and demonstrates that the proposed method outperforms the original NLM, as well as many previously developed methods.
TL;DR: Experimental results performed on real SAR images show the effectiveness of the proposed algorithm, in terms of detection performance and computational complexity, compared to classical methods.
Abstract: This paper investigates the problem of change detection in multitemporal synthetic aperture radar (SAR) images. Our motivation is to avoid using a large-size dense neighborhood around each pixel to measure its change level, which is usually considered by classical methods in order to perform their accurate detectors. Therefore, we propose to develop a pointwise approach to detect land-cover changes between two SAR images employing the principle of signal processing on graphs. First, a set of characteristic points is extracted from one of the two images to capture the image's significant contextual information. A weighted graph is then constructed to encode the interaction among these keypoints and hence capture the local geometric structure of this first image. With regard to this graph, the coherence of the information carried by the two images is considered for measuring changes between them. In other words, the change level will depend on how much the second image still conforms to the graph structure constructed from the first image. Additionally, due to the presence of speckle noise in SAR imaging, the log-ratio operator will be exploited to perform the image comparison measure. Experimental results performed on real SAR images show the effectiveness of the proposed algorithm, in terms of detection performance and computational complexity, compared to classical methods.
TL;DR: A novel FP reconstruction method under a gradient descent optimization framework that utilizes Poisson maximum likelihood for better signal modeling, and truncated Wirtinger gradient for effective error removal is proposed.
Abstract: Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging. Mathematically, Fourier ptychographic (FP) reconstruction can be implemented as a phase retrieval optimization process, in which we only obtain low resolution intensity images corresponding to the sub-bands of the sample’s high resolution (HR) spatial spectrum, and aim to retrieve the complex HR spectrum. In real setups, the measurements always suffer from various degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil location error, which would largely degrade the reconstruction. To efficiently address these degenerations, we propose a novel FP reconstruction method under a gradient descent optimization framework in this paper. The technique utilizes Poisson maximum likelihood for better signal modeling, and truncated Wirtinger gradient for effective error removal. Results on both simulated data and real data captured using our laser-illuminated FPM setup show that the proposed method outperforms other state-of-the-art algorithms. Also, we have released our source code for non-commercial use.
TL;DR: Experiments demonstrate that the proposed probability-based non-local means filter is competitive with other state-of-the-art speckle removal techniques and able to accurately preserve edges and structural details with small computational cost.
Abstract: In this Letter, a probability-based non-local means filter is proposed for speckle reduction in optical coherence tomography (OCT). Originally developed for additive white Gaussian noise, the non-local means filter is not suitable for multiplicative speckle noise suppression. This Letter presents a two-stage non-local means algorithm using the uncorrupted probability of each pixel to effectively reduce speckle noise in OCT. Experiments on real OCT images demonstrate that the proposed filter is competitive with other state-of-the-art speckle removal techniques and able to accurately preserve edges and structural details with small computational cost.
TL;DR: A new method for speckle reduction in 3D OCT is proposed, based on the assumption that neighboring A-scans are highly similar in the retina, and reconstructs each A-scan from its neighboring scans, which performs better than other methods.
Abstract: Optical coherence tomography (OCT) is a micrometer-scale, cross-sectional imaging modality for biological tissue. It has been widely used for retinal imaging in ophthalmology. Speckle noise is problematic in OCT. A raw OCT image/volume usually has very poor image quality due to speckle noise, which often obscures the retinal structures. Overlapping scan is often used for speckle reduction in a 2D line-scan. However, it leads to an increase of the data acquisition time. Therefore, it is unpractical in 3D scan as it requires a much longer data acquisition time. In this paper, we propose a new method for speckle reduction in 3D OCT. The proposed method models each $A$ -scan as the sum of underlying clean $A$ -scan and noise. Based on the assumption that neighboring $A$ -scans are highly similar in the retina, the method reconstructs each $A$ -scan from its neighboring scans. In the method, the neighboring $A$ -scans are aligned/registered to the $A$ -scan to be reconstructed and form a matrix together. Then low rank matrix completion using bilateral random projection is utilized to iteratively estimate the noise and recover the underlying clean $A$ -scan. The proposed method is evaluated through the mean square error, peak signal to noise ratio and the mean structure similarity index using high quality line-scan images as reference. Experimental results show that the proposed method performs better than other methods. In addition, the subsequent retinal layer segmentation also shows that the proposed method makes the automatic retinal layer segmentation more accurate. The technology can be embedded into current OCT machines to enhance the image quality for visualization and subsequent analysis such as retinal layer segmentation.
TL;DR: Wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images using supervised classifiers are reviewed.
TL;DR: Inspired by recent advances in machine learning algorithms such as robust PCA, this work aims to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys.
Abstract: Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise.
Aims. Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys.
Methods. We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO.
Results. Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. This three-term decomposition brings a detectability boost compared to the full-frame standard PCA approach, especially in the small inner working angle region where complex speckle noise prevents PCA from discerning true companions from noise.
TL;DR: The results show that the proposed de-noising method not only has a strong de-speckling ability, but also keeps the image details, such as the edge of a lesion, in the ultrasound images.
TL;DR: The presented approach enables automatic and high-performance change detection across a wide range of spatial scales (resolution levels) and follows a three-step approach of initial pre-processing; data enhancement/filtering; and wavelet-based, multi-scale change detection.
Abstract: Despite the significant progress that was achieved throughout the recent years, to this day, automatic change detection and classification from synthetic aperture radar (SAR) images remains a difficult task. This is, in large part, due to (a) the high level of speckle noise that is inherent to SAR data; (b) the complex scattering response of SAR even for rather homogeneous targets; (c) the low temporal sampling that is often achieved with SAR systems, since sequential images do not always have the same radar geometry (incident angle, orbit path, etc.); and (d) the typically limited performance of SAR in delineating the exact boundary of changed regions. With this paper we present a promising change detection method that utilizes SAR images and provides solutions for these previously mentioned difficulties. We will show that the presented approach enables automatic and high-performance change detection across a wide range of spatial scales (resolution levels). The developed method follows a three-step approach of (i) initial pre-processing; (ii) data enhancement/filtering; and (iii) wavelet-based, multi-scale change detection. The stand-alone property of our approach is the high flexibility in applying the change detection approach to a wide range of change detection problems. The performance of the developed approach is demonstrated using synthetic data as well as a real-data application to wildfire progression near Fairbanks, Alaska.
TL;DR: The novel idea of a physical-based despeckling, taking into account meaningful physical characteristics of the imaged scenes is introduced, via the implementation of aphysical-oriented probabilistic patch-based (PPB) filter based on a priori knowledge of the underlying topography and analytical scattering models.
Abstract: Speckle noise greatly limits both synthetic aperture radar (SAR) data human readability, especially for non-SAR-expert users, and performance of automatic processing and information retrieval procedures by computer programs. Therefore, despeckling of SAR images is an essential preprocessing step in SAR data analysis, processing, and modeling, as well as in information retrieval and inversion procedures. Up to now, one of the most accurate and promising despeckling approaches—among those based on a single SAR image—is the one relying on the nonlocal means concepts. However, at the best of our knowledge, most of the state of the art considers the despeckling problem only within a statistical framework, completely discarding the electromagnetic phenomena behind SAR imagery formation. In this paper, we introduce the novel idea of a physical-based despeckling, taking into account meaningful physical characteristics of the imaged scenes. This idea is realized via the implementation of a physical-oriented probabilistic patch-based (PPB) filter based on a priori knowledge of the underlying topography and analytical scattering models. This filter is suitable for SAR images of natural scenes presenting a significant topography. An adaptive version of the proposed scattering-based PPB filter for denoising of SAR images including both mountainous and flat areas is also developed. The performances of the proposed filter and its adaptive version are evaluated both qualitatively and quantitatively in numerical experiments using both simulated and actual SAR images. The proposed technique exhibits performance superior w.r.t. the standard PPB filter and comparable or, in some cases, superior to the state of the art, both in terms of speckle reduction and texture and detail preservation.
01 Nov 2016
TL;DR: A modified Canny algorithm where Gaussian smoothing is replaced by modified median filter that successfully removes speckle noise with little degradation of edges followed by weak weighted smoothing filter that in a controlled way removes other noise, again with insignificant damage to the edges is proposed.
Abstract: Ultrasound medical images are very important component of the diagnostics process. They are widely used since ultrasound is a non-invasive and non-ionizing diagnostics method. As a part of image analysis, edge detection is often used for further segmentation or more precise measurements of elements in the picture. Edges represent high frequency components of an image. Unfortunately, ultrasound images are subject to degradations, especially speckle noise which is also a high frequency component. That poses a problem for edge detection algorithms since filters for noise removal also degrade edges. Canny operator is widely used as an excellent edge detector, however it also includes Gaussian smoothing element that may significantly soften edges. In this paper we propose a modified Canny algorithm where Gaussian smoothing is replaced by modified median filter that successfully removes speckle noise with little degradation of edges followed by weak weighted smoothing filter that in a controlled way removes other noise, again with insignificant damage to the edges. Our proposed algorithm was tested on standard benchmark image and compared to other approaches from literature where it proved to be successful in precisely determining edges of internal organs.
TL;DR: Two-dimensional imaging using illumination via a single-mode fiber with a multiply scattering tip and compressed sensing acquisition and the imaging device is mechanically scan-free and insensitive to bending of the fiber, making it suitable for micro-endoscopy.
Abstract: We demonstrate two-dimensional imaging using illumination via a single-mode fiber with a multiply scattering tip and compressed sensing acquisition. We illuminate objects with randomly structured, but deterministic, speckle patterns produced by a coherent light source propagating through a TiO2-coated fiber tip. The coating thickness is optimized to produce speckle patterns that are highly sensitive to laser wavelength, yet repeatable. Images of the object are reconstructed from the characterized wavelength dependence of the speckle patterns and the wavelength dependence of the total light collected from the object using a single photodetector. Our imaging device is mechanically scan-free and insensitive to bending of the fiber, making it suitable for micro-endoscopy.
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TL;DR: The effectiveness of this new filtering method is demonstrated by comparing it to established speckle noise removal techniques on SAR images by developing the enhanced directional smoothing (EDS) technique.
Abstract: Synthetic aperture radar (SAR) images are subject to prominent speckle noise, which is generally considered a purely multiplicative noise process. In theory, this multiplicative noise is that the ratio of the standard deviation to the signal value, the "coefficient of variation," is theoretically constant at every point in a SAR image. Most of the filters for speckle reduction are based on this property. Such property is irrelevant for the new filter structure, which is based on directional smoothing (DS) theory, the enhanced directional smoothing (EDS) that removes speckle noise from SAR images without blurring edges. We demonstrate the effectiveness of this new filtering method by comparing it to established speckle noise removal techniques on SAR images.
01 Sep 2016
TL;DR: A multiobjective fuzzy clustering method for change detection in SAR images that obtains a set of solutions with different trade-off relationships between the two objectives, and users can choose one or more appropriate solutions according to requirements for diverse problems.
Abstract: Graphical abstractIn this paper, we put forward a multiobjective fuzzy clustering method for change detection in SAR images. As shown in the figure, the change detection problem is modeled as a multiobjective optimization problem, and two conflicting objective functions are constructed from the perspective of preserving detail and removing noise, respectively. A number of solutions representing different trade-off relationships between preserving detail and restraining noise are given by the proposed method. The decision makers can judge relatively and select one or more solutions according to the problem requirements. Display Omitted On account of the presence of speckle noise, the trade-off between removing noise and preserving detail is crucial for the change detection task in Synthetic Aperture Radar (SAR) images. In this paper, we put forward a multiobjective fuzzy clustering method for change detection in SAR images. The change detection problem is modeled as a multiobjective optimization problem, and two conflicting objective functions are constructed from the perspective of preserving detail and removing noise, respectively. We optimize the two constructed objective functions simultaneously by using a multiobjective fuzzy clustering method, which updates the membership values according to the weights of the two objectives to find the optimal trade-off. The proposed method obtains a set of solutions with different trade-off relationships between the two objectives, and users can choose one or more appropriate solutions according to requirements for diverse problems. Experiments conducted on real SAR images demonstrate the superiority of the proposed method.
TL;DR: The proposed unwrapping algorithm is able to process holographic phase data corrupted by non-Gaussian speckle decorrelation noise and exhibits better accuracy and shorter computation time, whereas others may fail to unwrap.
Abstract: Robust phase unwrapping in the presence of high noise remains an open issue. Especially, when both noise and fringe densities are high, pre-filtering may lead to phase dislocations and smoothing that complicate even more unwrapping. In this paper an approach to deal with high noise and to unwrap successfully phase data is proposed. Taking into account influence of noise in wrapped data, a calibration method of the 1st order spatial phase derivative is proposed and an iterative approach is presented. We demonstrate that the proposed method is able to process holographic phase data corrupted by non-Gaussian speckle decorrelation noise. The algorithm is validated by realistic numerical simulations in which the fringe density and noise standard deviation is progressively increased. Comparison with other established algorithms shows that the proposed algorithm exhibits better accuracy and shorter computation time, whereas others may fail to unwrap. The proposed algorithm is applied to phase data from digital holographic metrology and the unwrapped results demonstrate its practical effectiveness. The realistic simulations and experiments demonstrate that the proposed unwrapping algorithm is robust and fast in the presence of strong speckle decorrelation noise.
TL;DR: It is demonstrated that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of lesion features while effectively suppressing speckle noise.
Abstract: Goal: Effective speckle reduction in ultrasound B-mode imaging is important for enhancing the image quality and improving the accuracy in image analysis and interpretation. In this paper, a new feature-enhanced speckle reduction (FESR) method based on multiscale analysis and feature enhancement filtering is proposed for ultrasound B-mode imaging. In FESR, clinical features (e.g., boundaries and borders of lesions) are selectively emphasized by edge, coherence, and contrast enhancement filtering from fine to coarse scales while simultaneously suppressing speckle development via robust diffusion filtering. In the simulation study, the proposed FESR method showed statistically significant improvements in edge preservation, mean structure similarity, speckle signal-to-noise ratio, and contrast-to-noise ratio (CNR) compared with other speckle reduction methods, e.g., oriented speckle reducing anisotropic diffusion (OSRAD), nonlinear multiscale wavelet diffusion (NMWD), the Laplacian pyramid-based nonlinear diffusion and shock filter (LPNDSF), and the Bayesian nonlocal means filter (OBNLM). Similarly, the FESR method outperformed the OSRAD, NMWD, LPNDSF, and OBNLM methods in terms of CNR, i.e., 10.70 ± 0.06 versus 9.00 ± 0.06, 9.78 ± 0.06, 8.67 ± 0.04, and 9.22 ± 0.06 in the phantom study, respectively. Reconstructed B-mode images that were developed using the five speckle reduction methods were reviewed by three radiologists for evaluation based on each radiologist's diagnostic preferences. All three radiologists showed a significant preference for the abdominal liver images obtained using the FESR methods in terms of conspicuity, margin sharpness, artificiality, and contrast, p <0.0001. For the kidney and thyroid images, the FESR method showed similar improvement over other methods. However, the FESR method did not show statistically significant improvement compared with the OBNLM method in margin sharpness for the kidney and thyroid images. These results demonstrate that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of lesion features while effectively suppressing speckle noise.
TL;DR: A new iterative method is proposed to eliminate the speckle noise in iterative Fourier transform algorithms, and the result shows that high quality, low noise images can be achieved.
Abstract: Iterative Fourier transform algorithms are widely used for creating holograms in holographic image projection. However, the reconstructed image always suffers from the speckle noise severely due to the uncontrolled phase distribution of the image. In this paper, a new iterative method is proposed to eliminate the speckle noise. In the iteration, the amplitude and phase in the signal window in the output plane are constrained to the desired distribution and a special object-dependent quadratic phase distribution, respectively. Since the phase of the reconstructed image is assigned artificially, the speckle noise came from the destructive interference between the sampling points with random and erratic phase distribution can be eliminated. To verify the method, simulations and experiments are performed. And the result shows that high quality, low noise images can be achieved.
01 Aug 2016
TL;DR: A novel idea is proposed for successful identification of the brain tumor using normalized histogram and segmentation using K-means clustering algorithm and Naïve Bayes Classifier and Support Vector Machine so as to provide accurate prediction and classification.
Abstract: Magnetic resonance imaging (MRI) is a technique which is used for the evaluation of the brain tumor in medical science. In this paper, a methodology to study and classify the image de-noising filters such as Median filter, Adaptive filter, Averaging filter, Un-sharp masking filter and Gaussian filter is used to remove the additive noises present in the MRI images i.e. Gaussian, Salt & pepper noise and speckle noise. The de-noising performance of all the considered strategies is compared using PSNR and MSE. A novel idea is proposed for successful identification of the brain tumor using normalized histogram and segmentation using K-means clustering algorithm. Efficient classification of the MRIs is done using Naive Bayes Classifier and Support Vector Machine (SVM) so as to provide accurate prediction and classification.
TL;DR: This paper focuses on the despeckling of synthetic aperture radar (SAR) images by variational methods which introduce nonlocal regularization functionals, aiming at getting a better balance between the goodness of fit of the original data and the amount of smoothing.
Abstract: In this paper, we focus on the despeckling of synthetic aperture radar (SAR) images by variational methods which introduce nonlocal regularization functionals. To achieve this goal, two models are investigated from different aspects. The first model is derived for the logarithmically transformed (homomorphic) domain of the SAR data, and the other is derived for the original (nonhomomorphic) domain. The statistical properties of the speckle and the log-transformed speckle are analyzed, and the similarity measurements between pixels in the homomorphic domain and nonhomomorphic domain are then derived for constructing the corresponding nonlocal regularization functionals. Meanwhile, in the proposed models, we develop a strategy to adaptively choose the regularization parameters based on both the local heterogeneity information and the noise level of the images, aiming at getting a better balance between the goodness of fit of the original data and the amount of smoothing. A quasi-Newton iteration method is employed to quickly minimize the proposed adaptive nonlocal functionals. Experiments conducted on both simulated images and real SAR images confirm the good performances of the proposed methods, both in reducing speckle and preserving image quality.
TL;DR: A nonlocal total variation (NLTV)-based variational model for polarimetric synthetic aperture radar (PolSAR) data speckle reduction is proposed based on the Wishart fidelity term and the NLTV regularization defined for the complex-valued fourth-order tensor data.
Abstract: In this paper, we propose a nonlocal total variation (NLTV)-based variational model for polarimetric synthetic aperture radar (PolSAR) data speckle reduction. This model, named WisNLTV, is obtained based on the Wishart fidelity term and the NLTV regularization defined for the complex-valued fourth-order tensor data. Since the proposed model is non-convex, an equivalent bi-convex model is obtained using the property of conjugate functions. Then, an efficient iteration algorithm is developed to solve the equivalent bi-convex model, based on the alternating minimization and the forward-backward operator splitting technique. The proposed iteration algorithm is proved to be convergent under certain conditions theoretically and numerically. Experimental results on both synthetic and real PolSAR data demonstrate that the proposed method can effectively reduce speckle noise and, meanwhile, better preserve the details and the repetitive structures such as textures and edges, and the polarimetric scattering characteristics, compared with the other methods.